In a method of the aforementioned type, such objects are detected in a camera image which may constitute lane markings, by means of known image processing techniques. These recognized lane markings may be as well objects similar to markings such as guardrails, tar seams, and sides of the road. Therefore, preferably not all detected objects are tracked by means of tracking, but only a subset of said objects which were classified as “real” lane markings on the basis, for example, of additional sensor inputs or image processing techniques. In many applications it is sufficient to recognize the so-called own-lane on which the vehicle is currently travelling, and thus to include in the tracking only a left and a right limit marking of said own-lane. During the tracking, the local position and the local slope of model curves approximately describing the lane markings are predicted for a future point of time and adjusted with subsequent detections.
Recursive state estimators here generally serve the approximate projection of a system state into the future and are generally known in the art. Specifically, a state estimator used in the inventive method has a predictor-corrector structure.
The tracking of a left and a right lane marking by a single state estimator can be performed, for example, assuming that lane markings ahead of a vehicle are parallel to each other—which is the case in many actual traffic situations. Such a tracking process may reach a high degree of robustness, since the individual lane markings stabilize each other to some extent. However, there are traffic situations in which not all lane markings are parallel to each other, e.g. in existing turnoffs. In such situations, the model on which tracking is based cannot be brought in line with reality and the tracking algorithm fails. This may significantly reduce the performance of a driver assistance system, and is not acceptable particularly in autonomous vehicles.